Just trying a random included data set and seeing what happens.
Pulling MLS stadium locations from Wikipedia and mapping.
Adding stadium size and soccer specific detail
Looking at another data set
## Inverted geom defaults of fill and color/colour.
## To change them back, use invert_geom_defaults().
Making some tables using m1 from yesterday.
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 17.3996502 | 8.622659801 | 2.017898 | 5.179267e-02 |
| age | 0.1067703 | 0.008276623 | 12.900228 | 1.930596e-14 |
| Linear Mixed Effect Modeling of the Influence of Orange Tree Age on Tree Circumference | |||||
|---|---|---|---|---|---|
| Term | Estimate | Std. Error | Statistic | 95% C.I. | |
| 2.5% | 97.5% | ||||
| (Intercept) | 17.3996502 | 10.423695985 | 1.66924 | -3.96857308 | 38.7678794 |
| age | 0.1067703 | 0.005320996 | 20.06585 | 0.09617947 | 0.1173612 |
President’s Day
Tidy Tuesday - February 16, 2021
W. E. B. DuBois Visualizations
RM ANOVA on orange
It doesn’t work for this data set because the measurements are not all at the same times. Just use the LMEM from above.
EMMs
## size side type emmean SE df lower.CL upper.CL
## S L Std 835 2.2 24 830 840
## M L Std 850 2.2 24 845 855
## L L Std 763 2.2 24 759 768
## S R Std 817 2.2 24 812 821
## M R Std 842 2.2 24 837 846
## L R Std 787 2.2 24 782 791
## S L Octel 825 2.2 24 820 830
## M L Octel 822 2.2 24 817 826
## L L Octel 765 2.2 24 760 770
## S R Octel 820 2.2 24 815 825
## M R Octel 822 2.2 24 817 826
## L R Octel 775 2.2 24 770 780
##
## Confidence level used: 0.95
More EMMs
## size type side .wgt.
## 1 S Std L 3
## 2 M Std L 3
## 3 L Std L 3
## 4 S Octel L 3
## 5 M Octel L 3
## 6 L Octel L 3
## 7 S Std R 3
## 8 M Std R 3
## 9 L Std R 3
## 10 S Octel R 3
## 11 M Octel R 3
## 12 L Octel R 3
learnr?? writing functions?? datapasta?? augment?? tidymodels?? ## 20210223
Tidy Tuesday - February 23, 2021
Employed Status
Trying the name aesthetic in ggplot